Free NVIDIA NCA-GENL Exam Dumps Questions & Answers

When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

• A.  ReLU is a linear function while sigmoid is non-linear.
• B.  ReLU and sigmoid both have a range of 0 to 1.
• C.  ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
• D.  ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.

What metrics would you use to evaluate the performance of a RAG workflow in terms of the accuracy of responses generated in relation to the input query? (Choose two.)

• A.  Context precision
• B.  Retriever latency
• C.  Generator latency
• D.  Response relevancy
• E.  Tokens generated per second

Which metric is primarily used to evaluate the quality of the text generated by language models?

• A.  Precision
• B.  Perplexity
• C.  Recall
• D.  Accuracy

In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

• A.  Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.
• B.  Multi-head attention eliminates the need for positional encodings in the input sequence.
• C.  Multi-head attention reduces the model's memory footprint by sharing weights across heads.
• D.  Multi-head attention simplifies the training process by reducing the number of parameters.

When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

• A.  Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.
• B.  Subword tokenization removes punctuation and special characters to simplify text input.
• C.  Subword tokenization reduces the model's computational complexity by eliminating embeddings.
• D.  Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.